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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code>.make_regression</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-make-regression">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.make_regression</span></code></a></li>
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  <div class="section" id="sklearn-datasets-make-regression">
<h1><a class="reference internal" href="../classes.html#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code></a>.make_regression<a class="headerlink" href="#sklearn-datasets-make-regression" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.datasets.make_regression">
<code class="sig-prename descclassname">sklearn.datasets.</code><code class="sig-name descname">make_regression</code><span class="sig-paren">(</span><em class="sig-param">n_samples=100</em>, <em class="sig-param">n_features=100</em>, <em class="sig-param">n_informative=10</em>, <em class="sig-param">n_targets=1</em>, <em class="sig-param">bias=0.0</em>, <em class="sig-param">effective_rank=None</em>, <em class="sig-param">tail_strength=0.5</em>, <em class="sig-param">noise=0.0</em>, <em class="sig-param">shuffle=True</em>, <em class="sig-param">coef=False</em>, <em class="sig-param">random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/datasets/_samples_generator.py#L461"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.make_regression" title="Permalink to this definition">¶</a></dt>
<dd><p>Generate a random regression problem.</p>
<p>The input set can either be well conditioned (by default) or have a low
rank-fat tail singular profile. See <a class="reference internal" href="sklearn.datasets.make_low_rank_matrix.html#sklearn.datasets.make_low_rank_matrix" title="sklearn.datasets.make_low_rank_matrix"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_low_rank_matrix</span></code></a> for
more details.</p>
<p>The output is generated by applying a (potentially biased) random linear
regression model with <code class="docutils literal notranslate"><span class="pre">n_informative</span></code> nonzero regressors to the previously
generated input and some gaussian centered noise with some adjustable
scale.</p>
<p>Read more in the <a class="reference internal" href="../../datasets/index.html#sample-generators"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>n_samples</strong><span class="classifier">int, optional (default=100)</span></dt><dd><p>The number of samples.</p>
</dd>
<dt><strong>n_features</strong><span class="classifier">int, optional (default=100)</span></dt><dd><p>The number of features.</p>
</dd>
<dt><strong>n_informative</strong><span class="classifier">int, optional (default=10)</span></dt><dd><p>The number of informative features, i.e., the number of features used
to build the linear model used to generate the output.</p>
</dd>
<dt><strong>n_targets</strong><span class="classifier">int, optional (default=1)</span></dt><dd><p>The number of regression targets, i.e., the dimension of the y output
vector associated with a sample. By default, the output is a scalar.</p>
</dd>
<dt><strong>bias</strong><span class="classifier">float, optional (default=0.0)</span></dt><dd><p>The bias term in the underlying linear model.</p>
</dd>
<dt><strong>effective_rank</strong><span class="classifier">int or None, optional (default=None)</span></dt><dd><dl class="simple">
<dt>if not None:</dt><dd><p>The approximate number of singular vectors required to explain most
of the input data by linear combinations. Using this kind of
singular spectrum in the input allows the generator to reproduce
the correlations often observed in practice.</p>
</dd>
<dt>if None:</dt><dd><p>The input set is well conditioned, centered and gaussian with
unit variance.</p>
</dd>
</dl>
</dd>
<dt><strong>tail_strength</strong><span class="classifier">float between 0.0 and 1.0, optional (default=0.5)</span></dt><dd><p>The relative importance of the fat noisy tail of the singular values
profile if <code class="docutils literal notranslate"><span class="pre">effective_rank</span></code> is not None.</p>
</dd>
<dt><strong>noise</strong><span class="classifier">float, optional (default=0.0)</span></dt><dd><p>The standard deviation of the gaussian noise applied to the output.</p>
</dd>
<dt><strong>shuffle</strong><span class="classifier">boolean, optional (default=True)</span></dt><dd><p>Shuffle the samples and the features.</p>
</dd>
<dt><strong>coef</strong><span class="classifier">boolean, optional (default=False)</span></dt><dd><p>If True, the coefficients of the underlying linear model are returned.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None (default)</span></dt><dd><p>Determines random number generation for dataset creation. Pass an int
for reproducible output across multiple function calls.
See <a class="reference internal" href="../../glossary.html#term-random-state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array of shape [n_samples, n_features]</span></dt><dd><p>The input samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array of shape [n_samples] or [n_samples, n_targets]</span></dt><dd><p>The output values.</p>
</dd>
<dt><strong>coef</strong><span class="classifier">array of shape [n_features] or [n_features, n_targets], optional</span></dt><dd><p>The coefficient of the underlying linear model. It is returned only if
coef is True.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<div class="section" id="examples-using-sklearn-datasets-make-regression">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.make_regression</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-make-regression" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="This is an example showing the prediction latency of various scikit-learn estimators."><div class="figure align-default" id="id1">
<img alt="../../_images/sphx_glr_plot_prediction_latency_thumb.png" src="../../_images/sphx_glr_plot_prediction_latency_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/applications/plot_prediction_latency.html#sphx-glr-auto-examples-applications-plot-prediction-latency-py"><span class="std std-ref">Prediction Latency</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Ridge Regression is the estimator used in this example. Each color in the left plot represents ..."><div class="figure align-default" id="id2">
<img alt="../../_images/sphx_glr_plot_ridge_coeffs_thumb.png" src="../../_images/sphx_glr_plot_ridge_coeffs_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/linear_model/plot_ridge_coeffs.html#sphx-glr-auto-examples-linear-model-plot-ridge-coeffs-py"><span class="std std-ref">Plot Ridge coefficients as a function of the L2 regularization</span></a></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example we see how to robustly fit a linear model to faulty data using the RANSAC algor..."><div class="figure align-default" id="id3">
<img alt="../../_images/sphx_glr_plot_ransac_thumb.png" src="../../_images/sphx_glr_plot_ransac_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/linear_model/plot_ransac.html#sphx-glr-auto-examples-linear-model-plot-ransac-py"><span class="std std-ref">Robust linear model estimation using RANSAC</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Fit Ridge and HuberRegressor on a dataset with outliers."><div class="figure align-default" id="id4">
<img alt="../../_images/sphx_glr_plot_huber_vs_ridge_thumb.png" src="../../_images/sphx_glr_plot_huber_vs_ridge_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/linear_model/plot_huber_vs_ridge.html#sphx-glr-auto-examples-linear-model-plot-huber-vs-ridge-py"><span class="std std-ref">HuberRegressor vs Ridge on dataset with strong outliers</span></a></span><a class="headerlink" href="#id4" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We show that linear_model.Lasso provides the same results for dense and sparse data and that in..."><div class="figure align-default" id="id5">
<img alt="../../_images/sphx_glr_plot_lasso_dense_vs_sparse_data_thumb.png" src="../../_images/sphx_glr_plot_lasso_dense_vs_sparse_data_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.html#sphx-glr-auto-examples-linear-model-plot-lasso-dense-vs-sparse-data-py"><span class="std std-ref">Lasso on dense and sparse data</span></a></span><a class="headerlink" href="#id5" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="In this example, we give an overview of the sklearn.compose.TransformedTargetRegressor. Two exa..."><div class="figure align-default" id="id6">
<img alt="../../_images/sphx_glr_plot_transformed_target_thumb.png" src="../../_images/sphx_glr_plot_transformed_target_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/compose/plot_transformed_target.html#sphx-glr-auto-examples-compose-plot-transformed-target-py"><span class="std std-ref">Effect of transforming the targets in regression model</span></a></span><a class="headerlink" href="#id6" title="Permalink to this image">¶</a></p>
</div>
</div><div class="clearer"></div></div>
</div>


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